What is RAG (Search Augmented Generation)? | Explaining a new approach to generative AI

Yumi Ogawa

7 minutes to read

RAG (Retriever-Augmented Generation) is a new approach that brings innovation to modern generative AI technology known as "rag." Conventional generative AI is capable of generating text and images based on training data, but there are issues with the accuracy and reliability of responses to untrained questions.

RAG retrieves relevant information from a large dataset (Retriever), and then a generative model (Generator) generates text based on that information, enabling the provision of information with greater accuracy and precision than ever before.
In this article, we will explain the details of RAG.

The role and purpose of RAG in generative AI

RAG Definition and History

RAG (Retriever-Augmented Generation) retrieves information from external databases in real time in addition to the knowledge the model has in advance, and reflects this in the generated text, enabling the provision of content that reflects higher accuracy and the latest information.

The history of RAG has developed alongside the evolution of generative AI and information retrieval technology. In particular, recent advances in computing power and the availability of large-scale datasets have significantly improved the performance of RAG. By integrating new information with the internal knowledge of generative AI models, RAG is expected to have a wide range of applications, such as answering specialized questions in specific fields and solving engineering problems.

For example, if you want to increase productivity in business using generative AI, if you can incorporate internal information accumulated within your company as well as the latest external information, you will be able to eliminate incorrect or misleading information and utilize the generated output as more accurate, ready-to-use information.

The key to increasing the reliability of generative AI: the role of RAG

RAG (Retriever-Augmented Generation) plays an important role in generative AI models by increasing the accuracy and reliability of data. Generative AI generates content based on large amounts of data, but if the source of that data is unreliable, it will have a significant impact on the quality of the output.

Improving the reliability of generative AI requires the introduction and proper operation of RAGs. RAGs search and retrieve highly accurate data based on queries given to generative AI models, and then generate content based on this data. This process significantly improves the quality of generative AI output, enabling the provision of more useful and accurate information.

It also reduces "hallucination," which is when generative AI generates answers that are not based on facts and tells plausible lies.
Therefore, RAG is extremely important in systems that use generative AI.

Comparing RAG with other generative AI techniques

In this chapter, we will compare RAG in detail to see how it differs from other generative AI technologies and what advantages it has. First, we will compare RAG with GPT, the most well-known generative AI technology, and explain their differences and similarities. Next, we will compare RAG with general AI models and discuss the features and application areas of each technology.

Difference between RAG and GPT

GPT (Generative Pre-trained Transformer) is a model that is trained in advance using a large amount of text data to generate the most appropriate response to the input. It is based on the data up to the time of training, and cannot answer within the scope of the information up to the time of training.

RAG (Retrieval-Augmented Generation) integrates the generation capabilities of GPT with information search capabilities. It can generate responses based on the latest information by utilizing external databases and real-time searches. This overcomes the GPT's challenge of "difficulty in updating knowledge" while providing evidence for the generated content.

Comparison items	GPT	RAG
Knowledge Update	Knowledge at the time of training is the foundation; retraining is required to learn new information.	Knowledge can be easily updated by incorporating the latest information through external searches.
Reliability of information	It is difficult to directly justify a response.	External data sources can be cited to provide support for responses.
Response Content	Generates general and systematic answers based primarily on training data.	It takes search results and generates answers that are relevant to your specific context and situation.
Search function availability	Uses internal data only. No search function.	Utilizes external databases and internet searches to generate responses.
Usage scenarios	A wide range of natural language processing tasks including question answering, sentence generation, summarization, and translation.	Where you need a response with up-to-date information (FAQs, automated reporting, real-time analytics).
Computational resources	Uses large computational resources during training, but is efficient during inference.	Since an external search is involved, response speed depends on the performance of the search system.
Response Adaptability	Generate a response based solely on the prompts provided.	By combining external information and generative models, response content can be flexibly adapted.

As such, RAG is a more flexible and adaptable technology than GPT in that it can obtain the latest information in real time. Its true value is particularly evident in situations that require advanced data analysis, such as customer support, automated news generation, and the medical and financial sectors. By adopting RAG, companies and organizations can provide faster and more accurate information, thereby increasing their competitiveness.

▼What is ChatGPT?
⇒ ChatGPT｜Glossary

Comparison of RAG and general AI models

The advantage of RAG (Retriever-Generator) over general AI models is its highly accurate information generation and data acquisition capabilities.

While typical AI models generate information through trial and error, primarily based on pre-trained data, this approach has several limitations: For example, if the latest information on a particular topic changes frequently, it is difficult for a pre-trained model to incorporate the latest information.

On the other hand, RAG integrates two elements, a Retriever (information acquisition) and a Generator (generation), which allows for more effective data acquisition and the generation of accurate information based on that data. The Retriever accesses the latest data in real time and efficiently extracts the necessary information. The Generator then generates information based on the acquired data, enabling more accurate information generation than conventional AI models.

AI approach	overview	method	merit	Disadvantages	Application Examples
Supervised Learning	Learning patterns using data with correct answers	Regression (linear regression, polynomial regression), classification	- High learning accuracy - Fast learning speed	- Correct data is required - Depends on the quality of the data	Predictive models (finance, weather), email spam detection, image recognition
Unsupervised learning	Finding commonalities and patterns from data with no correct answers	Clustering, Association Analysis	- No correct answer data required - Discover unknown patterns in your data	- Human verification is required for the accuracy of the discovered rules - Can be difficult to interpret	Market segmentation, product recommendation (purchase pattern analysis), image classification
Reinforcement learning	Searching for the optimal solution through repeated trial and error	Reward-based learning (trial and error)	- Can be applied to optimization problems - Able to try and error at a speed faster than humans	- Computationally expensive - Complex design	Autonomous driving, robot control, game AI (Go, Shogi), cleaning robots
LLM(大規模言語モデル)	Deep learning is used to learn large amounts of text data and achieve natural language processing	Transformer-based deep learning	- Achieving advanced language understanding from large amounts of data - Adaptable to a variety of tasks (question answering, summarization, translation)	- Requires huge amounts of data and computational resources for training - Risks including bias and ethical issues	ChatGPT, translation tools, text generation (copywriting, automatic summarization), sentiment analysis
RAG (search extension generation)	Integrate with external databases and search systems to generate up-to-date responses	search (Retrieval) + Large-scale Language Model (LLM) Integration	- Utilize the latest information and external data - Easy knowledge update of the model - Ability to justify response	- Depends on the quality of external data - Response speed depends on search - Selection of external data affects accuracy	FAQ system, customer support, data-driven generation (reporting, real-time analysis)

▼What is transfer learning?
⇒ Transfer learning | Glossary

RAG's unique data acquisition and generation capabilities enable it to provide more effective solutions than other AI models, which is its greatest strength. Its true value is particularly evident in situations that require massive databases and real-time information. As a powerful hybrid system of information acquisition and generation, RAG is expected to be applied in a variety of fields.

RAG application examples

Retriever-Augmented Generation (RAG) has proven its potential in a wide range of applications. Its diverse uses bring great value to companies and organizations. Let's take a look at how RAG is being applied in each field.

Use in customer support

RAG is extremely useful in the area of customer support.
For example, a customer support system using RAG can automatically respond to FAQs and handle more complex issues. Rather than just providing predefined answers to specific queries, it can deepen its understanding of the issues customers are actually facing and present appropriate solutions. This allows for faster delivery of the information customers need and increased satisfaction with problem resolution.

By introducing RAG, the number of cases that need to be handled by human operators can be significantly reduced, optimizing resources. For example, by having RAG provide appropriate answers to routine inquiries, call center operators can focus on more complex issues or cases that require specialized attention. This improves support efficiency and reduces overall operational costs.

Furthermore, by continuing to learn based on past inquiry history and customer behavior data, it becomes possible to provide more personalized responses.

Applications in healthcare and finance

RAG technology has a wide range of applications in the healthcare and financial industries, primarily due to its advanced information capture and generation capabilities, which are ideally suited to these industries where accuracy and efficiency are essential.

For example, in the medical field, RAG is used to analyze patient diagnostic results and quickly develop treatment plans. Specifically, RAG can be used to extract relevant information from large amounts of medical papers and clinical data, improving diagnostic accuracy and quickly selecting the most appropriate treatment for each individual patient.

In the financial sector, RAG technology is also highly effective in analyzing market data and managing risk. For example, using RAG for real-time analysis of stock price trends, market forecasting, and risk assessment can be extremely useful in formulating investment strategies and avoiding risks.

It is also expected to streamline a wide range of operations, such as automatically generating financial reports and evaluating credit risk.

Advantages and challenges of RAG

RAG (Retriever-Augmented Generation) has attracted much expectation and interest in the field of generative AI. However, while this technology has many advantages, there are also challenges that must be overcome. This section will explore the advantages and challenges of RAG in detail. The major advantages of RAG are highly accurate data generation, cost reduction, and the potential for advanced personalization. However, challenges also exist, such as data bias issues and the complexity of understanding data. We will consider each of these points with specific examples.

The advantages of RAG in generative AI

First, a major advantage of RAG is that it can generate highly accurate data while reducing costs.
The reason behind this is the efficient acquisition and generation of information. RAG excels in its generative AI models, particularly in its ability to quickly and accurately gather necessary information and generate useful data based on that information.
RAG significantly streamlines the information search and data generation process, which traditionally takes a lot of time and resources.
By introducing RAG, companies can improve data processing efficiency in various fields and optimize resource utilization.

Challenges and how to overcome them

RAG may seem versatile, but it does have some challenges.
First, when using RAG, it is important to be aware of the issue of data bias. Generative AI relies on training data, and models based on biased data are likely to produce biased results. For example, if only data based on a particular region or culture is collected, it may produce inappropriate results for users with backgrounds different from that region or culture. To overcome the issue of data bias, it is important to use diverse datasets and check and evaluate from multiple angles.

Furthermore, understanding and utilizing RAGs requires advanced expertise, as the information acquisition and generation processes of RAGs are complex and require a deep understanding of their interactions.
For example, if the accuracy of information retrieval (Retriever) is low, the subsequent generation (Generator) process may not produce high-quality data, which will have a significant impact on the performance of the entire system. Effective use of RAG requires a deep understanding of the technology and operation while undergoing continuous learning and testing.

Future outlook and summary of RAG

RAG is expected to continue to attract attention as an innovative approach in the field of generative AI. Taking advantage of its advantages of highly accurate data generation and cost reduction, it is expected to be widely applied in various industries.
As RAG technology continues to evolve and become more practical, emphasis will likely be placed on overcoming issues such as bias and streamlining data management.
To address the issue of bias, it is important to use diverse training data and evaluate it regularly, and security measures and transparency are essential for data management.
By taking measures like these, RAG technology will become even more reliable and be put to practical use in many fields.

▼What is generative AI?
⇒Generative AI｜Glossary

The person who wrote the article

Affiliation: Marketing Department

Yumi Ogawa

After two years of experience as a copywriter at an advertising agency, she has been working in the IT industry ever since. Her experience at a variety of companies, from B2C to B2B, and from Japanese ventures to major foreign companies, is her strength. She has consistently worked in a variety of marketing-related roles, including public relations, branding, product marketing, and campaign management, and has been in her current position since May 2024. In her private life, she loves interacting with nature, hot springs, and public baths.
(Affiliations are as of the time of publication)